This paper presents a novel blockchain-based incentive system that protects farmers' privacy while promoting sustainable farming methods by using zero-knowledge proofs, or ZKPs. With this method, farmers can show that they've met their yield goals without giving away private production information. We implement the system as an Ethereum smart contract that uses cryptographic assurances to ensure correct reporting and distributes incentives based on verified crop yields. Our method addresses important problems in agricultural sustainability projects, like protecting privacy, the cost of verification, and making sure that rewards are shared fairly. The suggested answer affects sustainable agriculture policy, privacy-focused data exchange in supply lines, and the use of cryptographic methods in environmental governance.
Blockchain Technology Applications and Security
Smart Agriculture and AI
Physical Unclonable Functions (PUFs) and Hardware Security
Cloud storage systems have become central to data-driven industries due to their flexibility and scalability. However, ensuring the integrity and confidentiality of outsourced data remains a major concern, particularly in multi-tenant and dynamic cloud environments. This paper proposes a novel privacy-preserving framework that integrates Zero-Knowledge Proofs (ZKP), Pedersen Commitments, and bulk segmentation for efficient and scalable data integrity verification. Unlike traditional approaches, our framework enables Third-Party Auditors (TPAs) to verify cloud-stored data without exposing sensitive information. It is designed to support dynamic operations, detect insider and external threats proactively, and minimize computational overhead through segment-level auditing. Implementation and evaluation using Amazon S3 and DynamoDB demonstrate the framework’s practical viability, low communication cost, and robust tamper detection capabilities.
This paper presents a secure and privacy-preserving framework for Electronic Health Record (EHR) sharing using blockchain and zero-knowledge proofs (ZKPs). The system enables patients to control access to their health data through smart contracts, ensuring that only verified users can access sensitive information. ZKPs authenticate users without revealing identities, preserving confidentiality. IPFS is used for off-chain storage, reducing on-chain costs and improving scalability. The proposed model supports dynamic access control, including permission granting, revocation, and automatic expiry. This approach enhances data integrity, verifiability, and privacy in decentralized healthcare environments.
This research paper is a study of creating a blockchain-based system that would make data more secure and consistent in the decentralized information computing system. By pursuing qualitative exploratory research methodology, the study combines the findings of eight adaptively designed interviews with blockchain developers, enterprise IT managers, legal practitioners, and academic researchers. Thematic analysis disclosed five major dimensions defining secure blockchain architecture: Security Mechanisms, Scalability, Governance, Regulatory Compliance and Privacy & Confidentiality. Results indicate the need to focus on strong cryptographical protection, optimization, adaptive governance, legal alignment, and privacy-preserving protocols, including zero-knowledge proofs. Technical and institutional obstacles were identified by the stake holders, and it was noted that a balanced and modular framework that has capability of satisfying various operational and regulatory requirements is sought. This research adds a conceptual model informed by stakeholders and advises a simulation and implementation trial as the next stage to examine the applicability of the model in practice. The results present both theoretical and practical advice on the design of safe, scalable, and regulatory-compliant architectures of blockchain environments in distributed systems.
The Internet of Medical Things (IoMT) integrates interconnected medical devices and sensors to enable continuous patient monitoring and real-time healthcare delivery. Despite its transformative potential, IoMT systems face critical challenges related to data privacy, interoperability, latency, and security vulnerabilities inherent in centralized cloud architectures. Blockchain technology, with its decentralized ledger, cryptographic integrity, and smart contracts, has emerged as a promising solution to secure sensitive medical data while ensuring transparency and compliance with regulations such as HIPAA and GDPR. Concurrently, reinforcement learning (RL) techniques, especially advanced deep RL algorithms, facilitate intelligent, adaptive task offloading in fog-cloud computing environments to optimize latency, energy consumption, and resource allocation. This survey synthesizes twenty recent studies addressing blockchain-enabled privacy-preserving frameworks and RL-based task offloading mechanisms in IoMT. It critically evaluates architectural designs, cryptographic innovations including zero-knowledge proofs and quantum-resistant signatures, and RL methodologies for dynamic resource management. Key research challenges identified include the lack of standardized interoperability protocols across heterogeneous IoMT devices and blockchain platforms, the computational overhead of quantum-resistant cryptography on resource-constrained devices, and the opaque nature of RL models hindering clinical trust. Future research directions emphasize developing unified communication standards, lightweight post-quantum cryptographic schemes tailored for IoMT edge devices, and explainable RL frameworks to foster clinical adoption. Ultimately, this comprehensive analysis delineates a pathway toward robust, scalable, and secure IoMT ecosystems capable of delivering efficient, privacy-preserving healthcare services in complex digital infrastructures.
Daniel Escudero, Hong Cheng, H. M. Liu, Chaoping Xing · 5 authors
Reverse multiplication-friendly embeddings have played a crucial role in secure multiparty computation and zero-knowledge proofs. In this work, we generalize the notion of RMFEs todegree-DRMFEs. We present a general construction of degree-DRMFEs by generalizing the ideas on algebraic geometry used to construct traditional degree-2 RMFEs. Furthermore, our theory is given in a unified manner for general Galois rings, which include both rings of the form Zpkand fields like Fpk, which have been treated separately in prior works. We present multiple concrete sets of parameters for degree-DRMFEs (includingD= 2), which can be useful for future works. In the recent work of (Cheon & Lee, Eurocrypt’22), the concept of adegree-D packing methodwas formally introduced, which captures the idea of embedding multiple elements of a smaller ring into a larger ring. We show that the generalized notion of RMFEs todegree-D RMFEswhich, in spite of being “more algebraic” than packing methods, turn out to be essentially equivalent. Thus, our constructions of degree-DRMFEs are also degree-Dpacking methods.
In an era marked by increasingly sophisticated cyber threats and growing vulnerabilities in national critical infrastructure, this study explores the transformative role of confidential computing in defending against emerging cryptographic attacks and enabling secure threat intelligence sharing. Traditional cybersecurity measures, while effective for protecting data at rest and in transit, fall short in securing data during active processingan area exploited by advanced persistent threats, quantum computing, and side-channel attacks. This research investigates how hardware-based trusted execution environments (TEEs), homomorphic encryption, and zero-knowledge proofs embedded in confidential-computing platforms can preserve the confidentiality of sensitive operations even within potentially compromised environments. Through detailed case studies of major U.S. institutionsincluding PGandE, Exelon, JPMorgan Chase, Wells Fargo, and Kaiser Permanentethe paper demonstrates significant improvements in detection speed, false positive reduction, and operational efficiency. Furthermore, it proposes a scalable, privacy-preserving framework for collaborative cyber defense across critical sectors such as energy, finance, and healthcare. The findings underscore that integrating confidential computing with decentralized intelligence sharing networks not only enhances cybersecurity resilience but also yields substantial economic and regulatory benefits. This work advocates for a national, and eventually global, shift toward confidential-computing-enabled infrastructures to achieve robust, cooperative, and future-proof cyber defense ecosystems.
Weizhao Jin, Erik Kline, T. K. Satish Kumar, Lincoln Thurlow · 5 authors
Path validation in computer networks is used to enforce and verify data forwarding rules across network slices and administrative domains to satisfy specific service level requirements. Deviating from pre-established paths has the potential to downgrade network service quality, increase attack surface area, and disrupt network orchestration capabilities. Network operators regard the network infrastructure and topology as sensitive. This necessitates the need for privacy-preserving path validation techniques that leak minimal information about the overall network path to individual infrastructure owners. We present the design of a decentralized privacy-preserving path validation protocol using Non-Interactive Zero-Knowledge (NIZK) proofs to provide provable path privacy guarantees. The NIZK-based pairwise validation design identifies individual slice nodes that deviate from the prescribed path. Deploying this lightweight protocol periodically enables individual nodes to enforce and validate the network control path. We have implemented and evaluated our system on a testbed simulating a multi-authority network. Our results demonstrate the feasibility of preserving path privacy as well as the practicality of our proposed protocols for next-generation multi-authority sliced networks.
In blockchain systems, Merkle trees represent a fundamental cryptographic structure for verifying the validity of public keys in digital signatures. However, the verification process presents significant computational challenges, particularly when dealing with large-scale public key participation in signing operations. This paper focuses on addressing the efficiency bottlenecks in public key validity verification within Merkle tree commitments, with particular emphasis on their application in trustless cross-chain bridge protocols. While existing cross-chain solutions predominantly rely on zero-knowledge proofs for blockchain state validation, the inherent computational cost of proof generation remains prohibitive.We present a novel batch opening scheme for Merkle tree commitments that synergistically integrates Merkle tree construction from permutation arguments to verify the membership of extensive leaf sets. Our approach demonstrates remarkable proof generation efficiency advantages, particularly maintaining consistent performance regardless of the number of opened leaves, given a fixed tree depth. Our methods significantly reduce the computational overhead associated with public key validity verification. Meanwhile, it is fully applicable to the existing classical Merkle tree structure without any modifications and has universality.To demonstrate the practicality and efficiency of our scheme, We implemented the Merkle tree opening circuit for three hash functions (Poseidon, Rescue and Keccak) based on our scheme. Our evaluation shows that the batch opening scheme achieves better performance: proof generation time begins to shorten from an opening ratio of 0.25, achieving a 3.5 to 7.1× improvement at a ratio of 0.75 (with tree depth = 9). Similar improvements are also reflected in the proof size and verification time. Moreover, as tree depth increases, our method’s performance advantages become more pronounced.
S N Prajwalasimha, Dilip Kumar Jang Bahadur Saini, Nilesh Shelke, Amit Pimpalkar · 6 authors
Cyber-Physical Systems like smart grids, autonomous cars, and industrial IoT widely implement Federated Learning (FL) to provide distributed intelligence with privacy-protected data. Yet, the impending quantum threat makes conventional cryptographic methods in FL pipelines obsolete, exposing critical infrastructure to future security vulnerabilities. This paper presents Quantum-Resilient Federated Learning (QR-FL), a new framework integrating lattice-based post-quantum cryptography, light-weight zero-knowledge proofs, and trust-aware aggregation ensuring confidentiality, integrity, and quantum/classical attack resistance. Through comprehensive experimentation on real-world CPS datasets, QR-FL provides up to 48% enhanced adversarial robustness, 32% communication overhead savings, and 6.7% enhanced model accuracy compared to current state-of-the-art secure FL solutions. By achieving future-proof security with scalable federated intelligence, QR-FL provides an architecture foundation for future CPS, offering a landmark direction for secure, decentralized AI in the quantum age.
The Internet of Vehicles (IoV), as a core component of intelligent transportation systems, significantly enhances the intelligence level of traffic management by enabling efficient vehicle-to-vehicle (V2V) and vehicle-to-infrastructure information sharing. However, the highly dynamic and open nature of the IoV poses severe security challenges in cross-domain scenarios, mainly due to the lack of trust relationships between different domains, making it difficult to achieve efficient and secure cross-domain authentication(CDA). Existing CDA mechanisms in the IoT context often suffer from high computational complexity, excessive communication overhead, and poor scalability for large-scale deployments. This paper proposes a Batch CDA Protocol based on Dynamic Group Signatures (BCDAP-DGS) to address these issues. The proposed protocol incorporates non-interactive zero-knowledge (NIZK) proofs to achieve secure identity verification without requiring additional data exchange. By leveraging dynamic group signature techniques, BCDAP-DGS supports real-time updates of vehicle membership status and provides conditional anonymity. In addition, an online/offline authentication framework is designed by incorporating vehicle location information to precompute related parameters, thereby significantly improving CDA efficiency. A formal security analysis is conducted under the random oracle model, demonstrating that the proposed protocol satisfies essential security properties, including anonymity, non-frameability, unforgeability, and traceability. Experimental results and performance comparisons show that the proposed protocol outperforms existing schemes in terms of both security and efficiency, making it well-suited for large-scale and highly dynamic IoV CDA scenarios.
Academic publishing, integral to knowledge dissemination and scientific advancement, increasingly faces threats from unethical practices such as unconsented authorship, gift authorship, author ambiguity, and undisclosed conflicts of interest. While existing infrastructures like ORCID effectively disambiguate researcher identities, they fall short in enforcing explicit authorship consent, accurately verifying contributor roles, and robustly detecting conflicts of interest during peer review. To address these shortcomings, this paper introduces a decentralized framework leveraging Self-Sovereign Identity (SSI) and blockchain technology. The proposed model uses Decentralized Identifiers (DIDs) and Verifiable Credentials (VCs) to securely verify author identities and contributions, reducing ambiguity and ensuring accurate attribution. A blockchain-based trust registry records authorship consent and peer-review activity immutably. Privacy-preserving cryptographic techniques, especially Zero-Knowledge Proofs (ZKPs), support conflict-of-interest detection without revealing sensitive data. Verified authorship metadata and consent records are embedded in publications, increasing transparency. A stakeholder survey of researchers, editors, and reviewers suggests the framework improves ethical compliance and confidence in scholarly communication. This work represents a step toward a more transparent, accountable, and trustworthy academic publishing ecosystem.
Lingxu Guo, Hailong Wang, Jinbo Liu, Lei Wang · 6 authors
Power scheduling problem is a current research hotspot, and a meta-universe power trading operation system based on blockchain technology is proposed to address the problems of data silos, privacy leakage and low collaborative efficiency in traditional power scheduling system. By introducing homomorphic encryption, zero-knowledge proof and secure multi-party computing technology, it ensures the privacy protection of user data and realizes the safe aggregation and sharing of data. At the same time, Byzantine Fault Tolerance (PBFT) consensus mechanism is adopted to effectively resist the interference of potentially malicious nodes and safeguard the normal conduct of transactions and data security. In addition, by combining the Multi-Objective Particle Swarm Optimization (MOPSO) algorithm with smart contracts, the automation and decision-making optimization of power scheduling is realized, which significantly improves the operational efficiency of the scheduling system. The innovative data management scheme combining non-homogenized tokens (NFT) and interplanetary file system (IPFS) provides a secure and efficient data storage and transmission environment for the power trading market, which strongly protects the privacy and security of market data. This complete blockchain collaboration system has a broad development prospect and great application value for enhancing the security and operational efficiency of the power system in the context of meta-universe.
Blockchain technology has emerged as a promising solution for improving traceability across global supply chains, offering tamper-proof records and increased transparency.However, concerns related to data privacy, confidentiality, and interoperability continue to hinder widespread adoption.This paper proposes a comprehensive framework addressing these key challenges by combining privacy-preserving techniques-such as permissioned ledgers, zero-knowledge proofs, and verifiable credentials-with industry-driven data standards (GS1 EPCIS, W3C Verifiable Credentials).We first review the landscape of blockchain traceability solutions and outline critical requirements from regulatory and operational perspectives.Next, we detail our proposed privacy-preserving and interoperable architecture, incorporating off-chain storage, role-based permissions, and selective disclosure mechanisms to accommodate the diverse needs of modern supply chains.We illustrate these concepts through a high-level system design, accompanied by implementation considerations.Our evaluation highlights that successful adoption depends on carefully balancing transparency and confidentiality, supplemented by robust governance structures and standard APIs.The paper concludes by discussing future directions for blockchain traceability, emphasizing scalability, user-centric design, and cross-chain interoperability as critical enablers of a global, privacypreserving supply chain ecosystem.
Purpose: This research addresses critical limitations in existing blockchain-based data sharing solutions by developing an innovative framework integrating zero-knowledge proofs, homomorphic encryption, and smart contract automation for comprehensive big data privacy protection while maintaining utility and regulatory compliance. Methodology: A hierarchical distributed architecture comprising four layers was designed: data owner layer for encryption, blockchain network layer for consensus, privacy protection layer for cryptographic protocols, and application service layer for user interactions. Experimental evaluation was conducted on distributed networks with 20-100 nodes processing$100 ~\text{GB}-5 ~\text{TB}$datasets. Findings: The proposed framework achieves$\text{9 4. 1 \%}$privacy protection strength with$\text{2 2 \%}$computational efficiency improvement compared to existing approaches. The system supports 100 -node deployments while maintaining 131-158 TPS throughput, significantly outperforming traditional zero-knowledge implementations that achieve only 89.3 % privacy strength. Conclusion: The framework represents significant advancement in blockchain-based big data privacy protection, successfully balancing security guarantees with computational efficiency. Practical Implications: The solution demonstrates substantial value for healthcare, financial services, and IoT applications requiring secure collaborative analytics and enterprise-scale data sharing scenarios.
Haiwei Jiang, Xing Ji, Yixin Hou, Guangshuo Liu · 5 authors
The rapid growth of electric vehicles (EVs) raises challenges in securely aggregating sensitive charging data. Traditional centralized methods face privacy risks and lack trust. This paper proposes a trustworthy data aggregation method based on a dual-layer blockchain architecture combining a consortium chain and a public chain. Local aggregation and zero-knowledge proofs (ZKP) are performed on the consortium chain, while abstract summaries are stored on the public chain for verification and traceability. A trust-based consensus protocol and multi-level privacy controls ensure both efficiency and security. Experiments show the proposed method achieves 99.8 % verification accuracy, strong forgery detection (98.6 %), and high throughput (up to 530 TPS), making it suitable for smart EV charging platforms.
Fog Radio Access Networks (F-RAN) offload computational processes to the network edge and transmit the processed results to the cloud, significantly reducing the load on cloud servers and improving service efficiency. While this architecture offers convenience, it inevitably raises severe privacy concerns. To address these issues, this paper proposes a blockchain-integrated F-RAN architecture. It employs smart contracts to ensure the security of user requests and introduces Zero-Knowledge Proof (ZKP) technology to minimize the frequent transmission of user private information across the network, thereby providing more effective privacy protection for users.
With the continuous development of blockchain technology, massive off-chain data is mapped on the blockchain, ensuring the authenticity and privacy of on-chain data and off-chain data is a significant challenge. To solve this question, many studies use oracle to provide secure and reliable data for blockchain applications. Existing oracle schemes can protect the privacy of single-source data and prove the authenticity of private data sources to the third party. However, when handling multi-source data, these schemes require multiple executions to process and verify all data. We propose an optimized oracle scheme based on the “TLS-MPC” framework to improve efficiency. Firstly, we optimize the handshake process by dividing the$\mathrm{n}$servers into$\mathrm{t}$clusters and use the session ticket to reduce the number of MPC executions during the three-party handshake. As a result, most servers within each cluster run a fast three-party handshake by session ticket. Secondly, the prover runs two-party computation with the verifier to generate the queries and sends them to each data source to get the multi-source data. Then we design a constructable zero-knowledge proof system. Prover will inputs the multi-source data into the system to generate the proof value with a joint computation circuit. Finally, verifier will check the result sent from prover after the zero-knowledge proof is completed. Comparing with the DECO, our scheme is more efficient.
Traditional centralized scholarship evaluation processes typically require students to submit detailed academic records and qualification information, which exposes them to risks of data leakage and misuse, making it difficult to simultaneously ensure privacy protection and transparent auditability. To address these challenges, this paper proposes a scholarship evaluation system based on Decentralized Identity (DID) and Zero-Knowledge Proofs (ZKP). The system aggregates multidimensional ZKPs off-chain, and smart contracts verify compliance with evaluation criteria without revealing raw scores or computational details. Experimental results demonstrate that the proposed solution not only automates the evaluation efficiently but also maximally preserves student privacy and data integrity, offering a practical and trustworthy technical paradigm for higher education scholarship programs.
In the graph of an increasingly interconnected and data-driven world, strong, secure, and privacypreserving authentication systems come as cardinal. Traditional methods of authentication have rarely met the requirements in terms of security, scalability, and privacy of the user. The present models are prone to data breaches, unauthorized access, and opacity in authentication transactions. In this paper, we propose the BlockchainEnhanced Zero-Knowledge Proof Authentication model, where current blockchain technology is combined with state-of-the-art zero-knowledge proof protocols to improve security, privacy, and transparency. To this end, the BZKPA framework uses ZK-SNARKs and ZK-STARKs for performing private identity verification without the disclosure of sensitive sets of information. The aforesaid protocols guarantee compact, efficient, and scalable proof generation and verification processes. The model makes use of blockchain's distributed ledger technology to form an immutable and tamper-proof record for the purpose of tracing and integrity in authentication transactions. Inherent consensus mechanisms within blockchain enhance the security resilience of BZKPA against unauthorized access and cyber attacks. On the other hand, the workflow of BZKPA involves user registration and initialization of credentials, initiation of an authentication request, generation of zero-knowledge proof, verification of proof, and blockchain recording of valid authentication transactions. It means secure and privacy-preserving authentication, with transparency and auditability enabled by blockchain technology. Critical considerations addressed in the implementation of BZKPA shall be related to cryptographic security, blockchain platform selection, smart contract development, compliance with privacy and data protection, scalability, and interoperability. Applications of BZKPA range from financial services to healthcare, government, supply chain, and IoT, and even decentralized applications, providing enhanced security and privacy for a digital identity verification process. The model proposed by BZKPA offers several improvements in the authentication system, covering the famous limitations and lacunas of the previous methods and developing a strong solution for secure, transparent, and privacy-preserving authentication. Its adoption can be a new bar of digital identity verification, strengthen the defense of cybersecurity, and further empower users over their digital identities against varied contexts.
The deployment of artificial intelligence in healthcare is increasingly constrained by privacy, equity, and regulatory compliance challenges, especially in multilingual and cross-border contexts.Traditional centralized machine learning approaches are limited by restrictions on patient data sharing, raising both ethical and legal concerns.Federated learning offers a promising solution by enabling distributed training across institutions without transferring raw data, yet ensuring trust and privacy in federated systems remains a critical barrier.This study proposes a novel framework that combines transformer architectures with encrypted federated datasets anchored by blockchain zero-knowledge proofs (ZKPs) to achieve privacy-preserving, equitable, and multilingual healthcare diagnostics.Transformer-based models, known for their strength in natural language processing and multimodal learning, are adapted to operate on encrypted federated datasets spanning diverse linguistic and demographic contexts.Blockchain provides a decentralized trust layer, while zero-knowledge proofs ensure verifiable model updates without exposing sensitive patient information.This combination allows healthcare providers to collaboratively train diagnostic models that maintain strong predictive performance while adhering to strict privacy guarantees.The framework also advances health equity by enabling multilingual diagnostics that address disparities in underrepresented populations.By integrating explainability mechanisms, stakeholders gain insights into model reasoning across diverse cultural and linguistic datasets.Case applications in federated medical imaging, multilingual clinical notes, and genomic diagnostics highlight the framework's capacity to balance accuracy, privacy, and fairness.Overall, the integration of transformers, federated learning, and blockchain ZKPs represents a pathway toward trustworthy and equitable AI-driven healthcare, enabling collaborative innovation while safeguarding patient rights.
With the widespread adoption of Internet of Things (IoT) technologies in healthcare systems, security issues related to user privacy during data transmission and sharing have become increasingly prominent. To address these challenges, this paper proposes a medical privacy protection and secure sharing scheme based on Quantum Key Distribution (QKD). The scheme integrates multiple technologies, including blockchain, smart contracts, zero-knowledge proofs, and Chebyshev chaotic mapping, to ensure secure data sharing and access control among multiple communication entities. Compared with existing solutions, our approach enhances key management security through quantum keys and improves communication resilience against attacks by leveraging chaotic systems. User identity privacy is protected via zero-knowledge proofs. Under the random oracle model, the security of the proposed scheme is formally proven. Moreover, comparative experiments with existing protocols demonstrate the scheme's comprehensive advantages in terms of security and performance, evaluated across throughput, computational overhead, communication overhead, and storage overhead.